Graph neural networks (GNN) have shown great success in learn- ing from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection, and search. In these domains, the graphs are typically large and heterogeneous, containing many millions or billions of vertices and edges of different types. To tackle this challenge, we develop DistDGLv2, a system that extends DistDGL for training GNNs on massive heterogeneous graphs in a mini-batch fashion, using distributed hybrid CPU/GPU training. DistDGLv2 places graph data in distributed CPU memory and performs mini-batch computation in GPUs. For ease of use, DistDGLv2 adopts API compatible with Deep Graph Library (DGL)'s mini-batch training and heterogeneous graph API, which enables distributed training with almost no code modification. To ensure model accuracy, DistDGLv2 follows a synchronous training approach and allows ego-networks forming mini-batches to include non-local vertices. To ensure data locality and load balancing, DistDGLv2 partitions heterogeneous graphs by using a multi-level partitioning algorithm with min-edge cut and multiple balancing constraints. DistDGLv2 deploys an asynchronous mini- batch generation pipeline that makes computation and data access asynchronous to fully utilize all hardware (CPU, GPU, network, PCIe). We demonstrate DistDGLv2 on various GNN workloads. Our results show that DistDGLv2 achieves 2 - 3x speedup over DistDGL and 18× speedup over Euler. It takes only 5 - 10 seconds to complete an epoch on graphs with hundreds of millions of vertices on a cluster with 64 GPUs.
|Original language||English (US)|
|Title of host publication||KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - Aug 14 2022|
|Event||28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States|
Duration: Aug 14 2022 → Aug 18 2022
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Conference||28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022|
|Period||8/14/22 → 8/18/22|
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- distributed training
- graph neural networks